Background: Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data.

Results: We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy.

Conclusions: To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

RNAs are known to play essential roles in diverse cellular functions, extending well-beyond transfer of information from genes to proteins. RNA function is closely linked to its ability to fold into and convert between specific complex structures. Determining RNA structure has thus become a crucial step in understanding its function. Structure profiling experiments provide single nucleotide information on RNA structure. Recent advances in chemistry combined with application of new high-throughput sequencing techniques have enabled RNA structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. In this paper, we review current practices in analysis of structure profiling data, with emphasis on comparative and integrative analysis, as well as highlight emerging questions.

Background: The DNA strand displacement reaction, which uses flexible and programmable DNA molecules as reaction components, is the basis of dynamic DNA nanotechnology, and has been widely used in the design of complex autonomous behaviors.

Results: In this review, we first briefly introduce the concept of toehold-mediated strand displacement reaction and its kinetics regulation in pure solution. Thereafter, we review the recent progresses in DNA complex circuit, the assembly of AuNPs driven by DNA molecular machines, and the detection of single nucleotide polymorphism (SNP) using DNA toehold exchange probes in pure solution and in interface state. Lastly, the applications of toehold-mediated strand displacement in the genetic regulation and silencing through combining gene circuit with RNA interference systems are reviewed.

Conclusions: The toehold-mediated strand displacement reaction makes DNA an excellent material for the fabrication of molecular machines and complex circuit, and may potentially be used in the disease diagnosis and the regulation of gene silencing in the near future.

The controllable kinetics of strand displacement reaction, along with the programmability of DNA sequence, make DNA an excellent material for the fabrication of molecular machines and complex circuit, and may potentially be used in the disease diagnosis. In this review, we discuss the applications of toehold-mediated strand displacement in the constructions of exquisite molecular devices, complex functional circuits and reaction networks, and biomedical applications such as SNP discrimination and gene-induced disease detection and gene regulation.

Background: The therapeutic potential of bacteriophages has been debated since their first isolation and characterisation in the early 20^th century. However, a lack of consistency in application and observed efficacy during their early use meant that upon the discovery of antibiotic compounds research in the field of phage therapy quickly slowed. The rise of antibiotic resistance in bacteria and improvements in our abilities to modify and manipulate DNA, especially in the context of small viral genomes, has led to a recent resurgence of interest in utilising phage as antimicrobial therapeutics.

Results: In this article a number of results from the literature that have aimed to address key issues regarding the utility and efficacy of phage as antimicrobial therapeutics utilising molecular biology and synthetic biology approaches will be introduced and discussed, giving a general view of the recent progress in the field.

Conclusions: Advances in molecular biology and synthetic biology have enabled rapid progress in the field of phage engineering, with this article highlighting a number of promising strategies developed to optimise phages for the treatment of bacterial disease. Whilst many of the same issues that have historically limited the use of phages as therapeutics still exist, these modifications, or combinations thereof, may form a basis upon which future advances can be built. A focus on rigorous in vivo testing and investment in clinical trials for promising candidate phages may be required for the field to truly mature, but there is renewed hope that the potential benefits of phage therapy may finally be realised.

A bacteriophage (phage) is a virus that infects and replicates within bacteria, often leading to bacterial lysis and death. Phages that are able to efficiently kill specific bacterial pathogens have long been identified as having therapeutic potential in the context of treating bacterial disease in animals and humans; however the efficacies of phage therapies have rarely reached those of antibiotic drugs. This article aims to summarise recent developments in the field of phage engineering, where the tools of molecular biology and synthetic biology are being used to modify phages in ways that enhance or alter their natural antimicrobial function.

Background: Synthetic microbial consortia are conglomerations of genetically engineered microbes programmed to cooperatively bring about population-level phenotypes. By coordinating their activity, the constituent strains can display emergent behaviors that are difficult to engineer into isogenic populations. To do so, strains are engineered to communicate with one another through intercellular signaling pathways that depend on cell density.

Methods: Here, we used computational modeling to examine how the behavior of synthetic microbial consortia results from the interplay between population dynamics governed by cell growth and internal transcriptional dynamics governed by cell-cell signaling. Specifically, we examined a synthetic microbial consortium in which two strains each produce signals that down-regulate transcription in the other. Within a single strain this regulatory topology is called a “co-repressive toggle switch” and can lead to bistability.

Results: We found that in co-repressive synthetic microbial consortia the existence and stability of different states depend on population-level dynamics. As the two strains passively compete for space within the colony, their relative fractions fluctuate and thus alter the strengths of intercellular signals. These fluctuations drive the consortium to alternative equilibria. Additionally, if the growth rates of the strains depend on their transcriptional states, an additional feedback loop is created that can generate oscillations.

Conclusions: Our findings demonstrate that the dynamics of microbial consortia cannot be predicted from their regulatory topologies alone, but are also determined by interactions between the strains. Therefore, when designing synthetic microbial consortia that use intercellular signaling, one must account for growth variations caused by the production of protein.

Recently it has been shown that synthetic microbial consortia can use intercellular signaling pathways to create transcriptional regulatory topologies that mimic genetic circuits. However, if the strains within the consortium compete for resources, an added layer of complexity emerges. Here, we use computational modeling to explore the behavior of a two strain, transcriptionally co-repressive microbial consortium. We find that, unlike its genetic counterpart the bistable toggle switch, the co-repressive consortium can exhibit oscillatory behavior if the strains’ growth rates depend on protein production.

Background: The CRISPR-Cas system is a widespread prokaryotic defense system which targets and cleaves invasive nucleic acids, such as plasmids or viruses. So far, a great number of studies have focused on the components and mechanisms of this system, however, a direct visualization of CRISPR-Cas degrading invading DNA in real-time has not yet been studied at the single-cell level.

Methods: In this study, we fluorescently label phage lambda DNA in vivo, and track the labeled DNA over time to characterize DNA degradation at the single-cell level.

Results: At the bulk level, the lysogenization frequency of cells harboring CRISPR plasmids decreases significantly compared to cells with a non-CRISPR control. At the single-cell level, host cells with CRISPR activity are unperturbed by phage infection, maintaining normal growth like uninfected cells, where the efficiency of our anti-lambda CRISPR system is around 26%. During the course of time-lapse movies, the average fluorescence of invasive phage DNA in cells with CRISPR activity, decays more rapidly compared to cells without, and phage DNA is fully degraded by around 44 minutes on average. Moreover, the degradation appears to be independent of cell size or the phage DNA ejection site suggesting that Cas proteins are dispersed in sufficient quantities throughout the cell.

Conclusions: With the CRISPR-Cas visualization system we developed, we are able to examine and characterize how a CRISPR system degrades invading phage DNA at the single-cell level. This work provides direct evidence and improves the current understanding on how CRISPR breaks down invading DNA.

The CRISPR-Cas system is a widespread evolutionary adaptation in prokaryotes including archaea and bacteria, defending against invasive nucleic acids, such as plasmids or viruses. We aim to visualize and characterize how a CRISPR system acts within E. coli cells to destroy a phage invader at the single-cell level. By fluorescently labeling and tracking phage lambda DNA after infection using microscopy, we find that CRISPR rapidly degrades phage DNA to allow the cell to live on, and discover some parameters accounting for the cell-to-cell variability of the CRISPR functions, providing insights on how CRISPR systems protect bacteria.

Background: The tools of synthetic biology have enabled researchers to explore multiple scientific phenomena by directly engineering signaling pathways within living cells and artificial protocells. Here, we explored the potential for engineered living cells themselves to assemble signaling pathways for non-living protocells. This analysis serves as a preliminary investigation into a potential origin of processes that may be utilized by complex living systems. Specifically, we suggest that if living cells can be engineered to direct the assembly of genetic signaling pathways from genetic biomaterials in their environment, then insight can be gained into how naturally occurring living systems might behave similarly.

Methods: To this end, we have modeled and simulated a system consisting of engineered cells that control the assembly of DNA monomers on microparticle scaffolds. These DNA monomers encode genetic circuits, and therefore, these microparticles can then be encapsulated with minimal transcription and translation systems to direct protocell phenotype. The modeled system relies on multiple previously established synthetic systems and then links these together to demonstrate system feasibility.

Results: In this specific model, engineered cells are induced to synthesize biotin, which competes with biotinylated, circuit-encoding DNA monomers for an avidinized-microparticle scaffold. We demonstrate that multiple synthetic motifs can be controlled in this way and can be tuned by manipulating parameters such as inducer and DNA concentrations.

Conclusions: We expect that this system will provide insight into the origin of living systems as well as serve as a tool for engineering living cells that assemble complex biomaterials in their environment.

We have quantitatively explored the potential for engineering living cells to assemble and program synthetic gene networks in artificial protocells. We envision engineering living cells that control the assembly of linear DNA on a microparticle scaffold. Synthetic circuits could be encoded in this linear DNA. Artificial cells could then be created by encapsulating these scaffolds with a transcription-translation, cell-free expression reaction. Quantitative models of this process show that protocell expression could be tuned by altering the gene network motifs within the engineered living cells. These results demonstrate the potential for engineering ecosystems of living and artificial cells.

Background: The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network (ANN) has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine (SVM) is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem.

Methods: To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set (≥10 sequences) for model training and a test set (≥10 sequences) for model test.

Results: The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set (R²>0.99) and the test set (R²>0.98), both of which are better than that of ANN obtained by our previous work.

Conclusions: Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.

Machine learning models can learn knowledge from a given dataset and make predictions on unknown data. These technologies are widely used in artificial intelligence and made tremendous progress, triggering the coming era of “Industry 4.0”. In life sciences, introduction of such methods has greatly promoted the development of the discipline, especially modeling in bioinformatics and systems biology. As a powerful machine learning method suitable for small sample learning, support vector machine (SVM) was introduced into the field of promoter strength prediction. The good performance of SVM models demonstrates a promising application prospect of this method in prediction of promoter strength.

Background: A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. Many tools focus on two-dimensional space to display pathways in metabolic network. However, the usage of three-dimensional visualization may help to understand better the intricate topology of metabolic and regulatory networks.

Methods: We manually curated large amount of experimental data (including pathways, reactions and metabolites) from literature related with different types of engineered E. coli and then utilized a novel technology of three dimensional visualization to develop a comprehensive metabolic network named SynBioEcoli.

Results: SynBioEoli contains 740 biosynthetic pathways, 3,889 metabolic reactions, 2,255 chemical compoundsmanually curated from about 11,000 metabolism publications related with different types of engineered E. coli. Furthermore, SynBioEcoli integrates with various informatics techniques.

Conclusions: SynBioEcoli could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology. It could be accessed via web browsers (such as Google Chrome) supporting WebGL, at http://www.rxnfinder.org/synbioecoli/.

A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. The usage of three-dimensional visualization may help to understand better metabolic and regulatory networks than many tools which focus on two-dimensional space. Based on the large amount of experimental data manually curated from publications related with different types of engineered E. coli and novel technology of three dimensional visualization, SynBioEcoli was developed, which also integrates with various informatics techniques. It could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology.